This document discusses diagnosing and addressing bias and variance problems in machine learning models. It provides examples of high bias versus high variance, including learning curves. It recommends actions like getting more training data to address high variance, or trying additional features to address high bias. These include splitting data into training, cross validation, and test sets, and tuning regularization parameters. Examples are provided in MATLAB and additional resources on bias-variance and machine learning are referenced.
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Bias vs Variance
1. Bias vs. Variance
Machine Learning
Franco Cedillo
Digital Product Manager, tech researcher
iOS Provider at Thought Recap SFO
past: PM Digital at La República, Ing. Informático PUCP
2. Diagnosing bias vs. variance
¿El problema es bias o variance?
Cross validation set
Learning Curves
Caso Redes Neuronales
4. What should we try next?
Get more training examples
Try smaller sets of features
Try getting additional features
Try adding polynomial features
Try decreasing ƛ
Try increasing ƛ
13. Actions
Action Effect
Get more training examples Fixes high variance
Try smaller sets of features Fixes high variance
Try getting additional features Fixes high bias
Try adding polynomial features Fixes high bias
Try decreasing ƛ Fixes high bias
Try increasing ƛ Fixes high variance
15. Recursos Extra
Anotaciones de la lección
http://www.holehouse.org/mlclass/10_Advice_for_applying_machine_learning.html
Lección de la semana 6 en ML at Coursera
Andrew Ng
https://www.coursera.org/learn/machine-learning/home/week/6
Para una exposición clara vamos a tomar el caso de la Regresión Lineal
x1, x2, x3, x4, ...
(x1)^2, (x2)^2, x1.x2, ...
Contexto: Es un trabajo, estudio, tesis
Fecha límite
Con más complejidad la el aprendizaje sobre la data de entrenamiento se vuelve más precisa.
Así también se llega a acertar para el set de test, sin embargo con mayor complejidad se va perdiendo ese acierto.
Algunos resultados están mal
Alguien del equipo, un consultor, tal vez por parte del cliente obtiene gráficas que muestran un error grande
Alguien ve el código e intuye un error
No va a hacer nuestra tarea de implementar bien el algoritmo